Weather radar system and method with fusion of multiple weather information sources

ABSTRACT

A method of displaying an image representative of a weather condition near an aircraft includes receiving weather data representative of the weather condition from a plurality of weather data sources. The weather data includes location data for the weather condition. The method also includes mapping the weather data received from each source to a common locational reference frame based on the location data, adjusting the weather data received from each source to a common hazard scale, determining a hazard level associated with the weather condition for a reference point in the reference frame based on the adjusted weather data for each source, and displaying the image representative of the weather condition near the aircraft based on the hazard level for the reference point.

CROSS-REFERENCE TO RELATED PATENT APPLICATIONS

The present application is related to U.S. patent application Ser. No.14/162,035 filed on Jan. 23, 2014 by Kronfeld et al., entitled “WeatherRadar System and Method With Path Attenuation Shadowing,” U.S. patentapplication Ser. No. 14/086,844 filed on Nov. 21, 2013 by Breiholz etal., entitled “Weather Radar System and Method for Estimating VerticallyIntegrated Liquid Content,” and to U.S. patent application Ser. No.14/465,730 filed on Aug. 21, 2014 by Breiholz et al., entitled “WeatherRadar System and Method With Latency Compensation for Data Link WeatherInformation,” each of which is assigned to the assignee of the presentapplication and incorporated herein by reference in its entirety.

BACKGROUND

The present disclosure relates generally to the field of weather displaysystems. More particularly, the present disclosure relates to a weatherdisplay system and method configured to provide latency compensation fordata linked weather data.

Aircraft weather radar systems are often used to alert operators ofvehicles, such as aircraft pilots, of weather hazards in the area nearthe aircraft. Such weather radar systems typically include an antenna, areceiver transmitter, a processor, and a display. The system transmitsradar pulses or beams and receives radar return signals indicative ofweather conditions. Conventional weather radar systems, such as the WXR2100 MULTISCAN radar system manufactured by Rockwell Collins, Inc., haveDoppler capabilities and can measure or detect parameters such asweather range, weather reflectivity, weather velocity, and weatherspectral width or velocity variation. Weather radar systems may alsodetect outside air temperature, winds at altitude, INS G loads (in-situturbulence), barometric pressure, humidity, etc.

Weather radar signals are processed to provide graphical images to aradar display. The radar display is typically a color display providinggraphical images in color to represent the severity of the weather. Someaircraft systems also include other hazard warning systems such as aturbulence detection system. The turbulence detection system can provideindications of the presence of turbulence or other hazards. Conventionalweather display systems are configured to display weather data in twodimensions and often operate according to ARINC 453 and 708 standards. Ahorizontal plan view provides an overview of weather patterns that mayaffect an aircraft mapped onto a horizontal plane. Generally thehorizontal plan view provides images of weather conditions in thevicinity of the aircraft, such as indications of precipitation rates.Red, yellow, and green colors are typically used to symbolize areas ofrespective precipitation rates, and black color symbolizes areas of verylittle or no precipitation. Each color is associated with radarreflectivity range which corresponds to a respective precipitation raterange. Red indicates the highest rates of precipitation while greenrepresents the lowest (non-zero) rates of precipitation. Certaindisplays may also utilize a magenta color to indicate regions ofturbulence.

While aircraft-based weather radar systems may typically provide themost timely and directly relevant weather information to the aircraftcrew based on scan time of a few seconds, the performance ofaircraft-based weather systems may be limited in several ways. First,typical radar beam widths of aircraft-based weather radar systems are 3to 10 degrees. Additionally, the range of aircraft-based weather radarsystems is typically limited to about 300 nautical miles, and typicallymost effective within about 80-100 nautical miles. Further,aircraft-based weather radar systems may be subject to ground clutterwhen the radar beam intersects with terrain, or to path attenuation dueto intense precipitation or rainfall.

Information provided by aircraft weather radar systems may be used inconjunction with weather information from other aircraft or ground-basedsystems to, for example, improve range and accuracy and to reduce gapsin radar coverage. For example, the National Weather Service WSR-88DNext Generation Radar (NEXRAD) weather radar system is conventionallyused for detection and warning of severe weather conditions in theUnited States. NEXRAD data is typically more complete than data fromaircraft-based weather radar systems due to its use of volume scans ofup to 14 different elevation angles with a one degree beam width.Similarly, the National Lightning Detection Network (NLDN) may typicallybe a reliable source of information for weather conditions exhibitingintense convection. Weather satellite systems, such as the GeostationaryOperational Environmental Satellite system (GOES) and Polar OperationalEnvironmental Satellite system (POES) are other sources of data used forweather analyses and forecasts.

While NEXRAD has provided significant advancements in the detection andforecasting of weather, NEXRAD data may have gaps where no data iscollected (e.g., due to cone of silence and umbrella of silence regions,insufficient update rates, geographic limitations, or terrainobstructions). Similarly, weather observations and ground infrastructureare conventionally limited over oceans and less-developed land regions.Providing weather radar information from aircraft systems to otheraircraft systems and/or ground-based operations may provide significantimprovement to weather observations and forecasts by filling such gapsin radar coverage. Similarly, providing weather radar information fromground-based systems to aircraft-based systems may increase the rangeand accuracy of aircraft-based systems in certain conditions.

One issue with sharing weather data among aircraft-based andground-based weather radar systems is the discrepancy in apparentlocation of weather conditions due to the time delay associated withtransmitting and displaying shared weather data. For example, dependingon circumstances, latencies associated with transmission and delivery ofuplinked weather radar data from a ground-based system to anaircraft-based system are commonly on the order of 5-10 minutes, and insome cases may be as long as 20 minutes. Ground-based weather radarsystems may also have a lower update rate. Such latency issues haveconventionally limited the use of uplinked weather radar data to longerrange applications rather than shorter range tactical decisions.

Such latency issues are further compounded when multiple weather radardata sources are combined. Current systems typically provide individualdisplays for each data source, and often with display limitations thatrequire the aircraft crew to form a mental image of an integrateddisplay rather than actually viewing an integrated display. Furthermore,misalignments due to latency issues may result in increasing the size ofthreat regions such that longer diversions are required for an aircraftto avoid such enlarged threat regions. In addition, the various sensorsused by each of the multiple weather data sources may provide dissimilarmeasurements for a weather condition, creating uncertainty in how toweight the data provided from each source. There is an ongoing need forimproved weather radar systems and methods that provide a comprehensiveintegrated display of weather conditions using multiple weather radardata sources. There is yet further need for improved weather radarsystems and methods that compensate for latency issues associated withsharing and fusing weather radar data among aircraft-based andground-based systems to more accurately portray the threat posed by aweather condition. There is further need for improved weather radarsystems and methods that provide a confidence factor reflecting thedegree to which various input sources agree in their characterization ofthe severity of the threat posed by a weather condition. There isfurther need for improved weather radar systems and methods that providea composite estimate of storm top height to provide a more accuratepicture of the vertical extent of the threat posed by a weathercondition.

SUMMARY

According to an exemplary embodiment, a method of displaying an imagerepresentative of a weather condition near an aircraft includesreceiving weather data representative of the weather condition from aplurality of weather data sources. The weather data includes locationdata for the weather condition. The method also includes mapping theweather data received from each source to a common locational referenceframe based on the location data, adjusting the weather data receivedfrom each source to a common hazard scale, determining a hazard levelassociated with the weather condition for a reference point in thereference frame based on the adjusted weather data for each source, and

displaying the image representative of the weather condition near theaircraft based on the hazard level for the reference point

According to another exemplary embodiment, an aircraft weather radarsystem includes a processor and a non-transitory memory coupled to theprocessor. The memory contains program instructions that, when executed,cause the processor to receive weather data representative of a weathercondition from a plurality of weather data sources. The weather dataincludes location data for the weather condition. The instructionsfurther cause the processor to map the weather data received from eachsource to a common locational reference frame based on the locationdata, determine a hazard level associated with the weather condition fora reference point in the reference frame based on the adjusted weatherdata for each source, evaluate a confidence level for the hazard levelat the reference point, and display an image representative of theweather condition near the aircraft based on the confidence level forthe hazard level at the reference point.

According to another exemplary embodiment, a weather radar systemincludes a processor and a non-transitory memory coupled to theprocessor. The memory contains program instructions that, when executed,cause the processor to receive echo top data for a weather conditionfrom first and second weather data sources, map the echo top data to acommon reference frame, adjust the echo top data received from the firstsource based on the echo top data received from the second source, andgenerate an image representative of the weather condition based on theadjusted echo top data.

BRIEF DESCRIPTION OF THE DRAWINGS

The disclosure will become more fully understood from the followingdetailed description, taken in conjunction with the accompanyingfigures, wherein like reference numerals refer to like elements, inwhich:

FIG. 1A is a perspective view schematic of an exemplary aircraft controlcenter or cockpit;

FIG. 1B is a side view schematic illustration of the front of anexemplary aircraft with an aircraft control center and nose;

FIG. 2 is a more detailed block diagram of the exemplary weather radarsystem of FIG. 1;

FIG. 3 is a diagram of an exemplary aircraft communications system;

FIG. 4A is a block diagram of an exemplary weather radar system;

FIG. 4B is a data flow diagram of an exemplary weather radar system;

FIG. 5 is a flow diagram of an exemplary process for providing imagedata indicative of a weather condition;

FIG. 6 is a diagram of an exemplary gridded weather data structure;

FIG. 7 is a flow diagram of an exemplary process for adjusting weatherdata to update a location of a weather condition;

FIG. 8 is a flow diagram of an exemplary process for selecting amongweather data sources in order to update a location of a weathercondition;

FIG. 9 is a flow diagram of an exemplary process for updating thelocation of weather cells not having motion vector or tracking data;

FIG. 10 is an exemplary translation chart of selected parameters to theVIP hazard scale; and

FIGS. 11A-11F are diagrams illustrating exemplary approaches to fusionof echo top height data.

DETAILED DESCRIPTION

Before turning to the figures, which illustrate the exemplaryembodiments in detail, it should be understood that the application isnot limited to the details or methodology set forth in the descriptionor illustrated in the figures. It should also be understood that theterminology is for the purpose of description only and should not beregarded as limiting. As discussed below, the systems and methods can beutilized in a number of display devices for various types ofapplications or sensing systems. In some embodiments, the systems andmethods of the present disclosure may be used for a flight display of anaircraft. According to various other exemplary embodiments, the systemsand methods of the present disclosure may be used by any system in anyother embodiment for rendering computer graphics and displaying anoutput (e.g., in another aircraft or spacecraft, a ground-based vehicle,or in a non-vehicle application such as a ground-based weather radarsystem.

Referring to FIG. 1A, an exemplary aircraft control center or cockpit 10for an aircraft is shown. Aircraft control center 10 may include one ormore flight displays 20. Flight displays 20 may be implemented using anyof a variety of display technologies, including CRT, LCD, organic LED,dot matrix display, and others. Flight displays 20 may be navigation(NAV) displays, primary flight displays, electronic flight bag displays,tablets such as iPad® computers manufactured by Apple, Inc. or tabletcomputers, synthetic vision system displays, head up displays (HUDs)with or without a projector, wearable displays, Google glasses, etc.Flight displays 20 may be used to provide information to the flightcrew, thereby increasing visual range and enhancing decision-makingabilities. One more flight displays 20 may be configured to function as,for example, a primary flight display (PFD) used to display altitude,airspeed, vertical speed, navigation and traffic collision avoidancesystem (TCAS) advisories. One or more flight displays 20 may also beconfigured to function as, for example, a multi-function display used todisplay navigation maps, weather radar, electronic charts, TCAS traffic,aircraft maintenance data and electronic checklists, manuals, andprocedures. One or more flight displays 20 may also be configured tofunction as, for example, an engine indicating and crew-alerting system(EICAS) display 110 c used to display critical engine and system statusdata. Other types and functions flight displays 20 are contemplated aswell. According to various exemplary embodiments, at least one of flightdisplays 20 may be configured to provide a rendered display from thesystems and methods of the present disclosure.

In some embodiments, flight displays 20 may provide an output from anaircraft-based weather radar system, LIDAR system, infrared system orother system on the aircraft. For example, flight displays 20 mayinclude a weather display, a joint display, a weather radar map and aterrain display. Further, flight displays 20 may include an electronicdisplay or a synthetic vision system (SVS). For example, flight displays20 may include a display configured to display a two-dimensional (2-D)image, a three dimensional (3-D) perspective image of terrain and/orweather information, or a four dimensional (4-D) display of weatherinformation or forecast information. Other views of terrain and/orweather information may also be provided (e.g., plan view, horizontalview, vertical view, etc.). The views may include monochrome or colorgraphical representations of the terrain and/or weather information.Graphical representations of weather or terrain may include anindication of altitude of the weather or terrain or the altituderelative to the aircraft.

Aircraft control center 10 may include one or more user interface (UI)elements 22. UI elements 22 may include, for example, dials, switches,buttons, touch screens, keyboards, a mouse, joysticks, cursor controldevices (CCDs) or other multi-function key pads certified for use withavionics systems, etc. UI elements 22 may be configured to, for example,allow an aircraft crew member to interact with various avionicsapplications and perform functions such as data entry, manipulation ofnavigation maps, and moving among and selecting checklist items. Forexample, UI elements 22 may be used to adjust features of flightdisplays 20, such as contrast, brightness, width, and length. UIelements 22 may also (or alternatively) be used by an occupant tointerface with or change the displays of flight displays 20. UI elements22 may additionally be used to acknowledge or dismiss an indicatorprovided by flight displays 20. Further, UI elements 22 may be used tocorrect errors on the electronic display. Other UI elements 22, such asindicator lights, displays, display elements, and audio alertingdevices, may be configured to warn of potentially threatening conditionssuch as severe weather, terrain, obstacles, etc.

Referring to FIG. 1B, a side-view of an exemplary aircraft 30 withaircraft control center 10 and a nose 40 is shown. In the illustratedembodiment, a radar system 50, such as a weather radar system or otherradar system, is generally located inside nose 40 of aircraft 30 orinside a cockpit of aircraft 30. According to other exemplaryembodiments, radar system 50 may be located anywhere on aircraft 30,such as on the top of aircraft 30 or on the tail of aircraft 30.Furthermore, the various components of radar system 50 may bedistributed at multiple locations throughout aircraft 30. Additionally,radar system 50 may include or be coupled to an antenna system ofaircraft 30. Radar system 50 or other equipment aboard aircraft 30 mayalso be configured to receive weather data from other sources. Radarsystem 50 may be configured to detect or receive data for the systemsand methods of the present disclosure. According to exemplaryembodiments, radar system 50 may be an RTA-4218 MULTISCAN radar system,a WXR-2100 MULTISCAN radar system, or similar system manufactured byRockwell Collins Inc., and configured in accordance with the principlesdescribed herein.

Radar system 50 may generally work by sweeping a radar beam horizontallyback and forth across the sky. For example, radar system 50 may conducta first horizontal sweep 52 directly in front of the aircraft and asecond horizontal sweep 54 downward at a tilt angle 56 (e.g., 20 degreesdown). Returns from different tilt angles may be electronically mergedto form a composite image for display on an electronic display, such asa flight display 20 in aircraft control center 10. Returns may also beprocessed to, for example, distinguish among terrain, weather, and otherobjects, to determine the height of the terrain, to determine the heightof the weather, etc.

Radar system 50 may also sweep a radar beam vertically back and forth atvarying vertical tilt angles. Results from the different vertical tiltangles may be analyzed to determine the characteristics of weather. Forexample, the altitude, range, and vertical height of weather may bedetermined using the vertical scan results. The vertical scan resultsmay be used to form an image for display on an electronic display (e.g.,flight display 20, etc.). For example, a vertical profile view of theweather may be generated. The profile may be used by a pilot todetermine height, range, hazards and threats, and other relevantinformation that may be utilized by an aircraft crew member to changethe course of the aircraft to avoid the detected weather condition.

Referring to FIG. 2, a block diagram of an exemplary weather detectionsystem 200 that may be used, for example, on an aircraft 201 or othervehicle is shown. System 200 may include a weather radar system 202(e.g., a system similar to radar system 50), aircraft sensors 203,electronics (such as a processor 204), an electronic display system 206(e.g., a display similar to flight display 20), and a communicationsystem 208. Weather radar system 202 is generally configured to cast oneor more radar beams from an aircraft mounted antenna, to receivereturns, and to interpret the returns (e.g. for display to a user, fortransmission to an external weather system, etc.).

Additionally, weather radar system 202 may perform multiple radarsweeps. The radar sweeps may include horizontal sweeps, vertical sweeps,or a combination of horizontal and vertical sweeps. Further, the radarsweeps can be performed such that they are substantially orthogonal toone another. According to other exemplary embodiments, weather radarsystem 202 can be a monopulse radar system, a sequential lobing system,or a radar system with an aperture capable of switching modes. Aircraftsensors 203 may include, for example, one or more lightning sensors,turbulence sensors, pressure sensors, optical systems (e.g., camerasystem, infrared system), outside air temperature sensors, winds ataltitude sensors, INS G load (in-situ turbulence) sensors, barometricpressure sensors, humidity sensors, or any other aircraft sensors orsensing systems that may be used to monitor weather and detect, forexample, lightning, convective cells, clear air turbulence, etc. Datafrom aircraft sensors 203 may be output to processor 204 for furtherprocessing and display, or for transmission to a station 220 (e.g., aground-based weather radar system or terrestrial station) or to otheraircraft 230, 240 via communication system 208.

Weather radar system 202 may be a system for detecting weather patterns.Detected weather patterns may be communicated to electronic displaysystem 206 for display to the flight crew. In addition, data fromstation 220 may be displayed on display system 206. Detected weatherpatterns may instead or may also be provided to electronics or processor204 for further analysis or transmission to a station 220 or anotheraircraft 230, 240 via communication system 208.

Station 220 may direct the aircraft 201, 230, 240 via communicationsystem 208 to scan in specific areas to improve detection accuracy ofweather. Alternatively, system 202 may request that station 220 andaircraft 230, 240 direct a scan towards weather of interest to aircraft201 (e.g., in the flight path) to improve weather detection accuracy.The scans performed by radar system 202 and the requests may betransmitted to station 220 or another aircraft 230, 240 viacommunication system 208.

Referring to FIG. 3, an exemplary aircraft communications system 300 isshown. System 300 may facilitate communications among an aircraft 301having weather radar system 302 aboard, a ground-based data center orterrestrial station 320 and other aircraft, such as an aircraft 330 andan aircraft 340. Station 320 may receive weather data via a channel 342from aircraft 301, via a channel 344 from aircraft 330, and via achannel 346 from aircraft 340. System 300 may utilize data andcommunications from more than three aircraft even though only threeaircraft are shown in FIG. 3. Additional data may be received fromground based radar 350 from a wireless or wired channel. Station 320 mayprovide data to aircraft 301 via a channel 372, to aircraft 330 viachannel 374, and to aircraft 340 via channel 376. Station 320 may alsoprovide scheduling data and other control data to aircraft 301 via achannel 382, to aircraft 330 via a channel 384, and to aircraft 340 viaa channel 386.

Various types of channels may be utilized including virtual channels,radio channels, satellite channels, etc. The channels may bebi-directional or uni-directional. Channels may be satellite linkchannels, VHF channels, INMARSAT channels, etc. Any type of wirelesscommunications may be utilized. Various types of communicationprotocols, including network and ad hoc network protocols may be used toperform communication operations and establish the channels in FIG. 3.

The weather data exchanged among ground station 320 and aircraft 301,330, and 340 may be in a number of forms. For example, the weather datamay include radar data containing location information, motion vectordata, time of sensing information, and measured parameter values for aweather condition 390. The location information may be in, for example,a format based on azimuth, elevation, and range from the radar system oranother fixed reference point, in a rectangular grid format, ageoregistered format, or other format. The radar data may also includeradar characteristics associated with the radar used to provide theradar data. The characteristics may include an indication of band-type,radar quality, tilt angle, etc. In some embodiments, station 320 mayadjust radar for its particular bands so that comparisons and selectionof data is consistent.

In some embodiments, the weather data may be provided from a pluralityof sources. Such weather data may also be indicative of one or moretypes of weather conditions. For example, weather data may be indicativeof convective weather systems (e.g., thunderstorms), turbulence, windsaloft, icing, and/or volcanic ash. In some embodiments, data regardingconvective weather systems may be provided from a ground-based weathersystem such as NEXRAD. Such data may include IDs for an adaptable numberof weather cells, which may be segmented (e.g., delivered in polygonformat) weather cells identified in a series of radar volume scans.Individual weather cells may be, for example, 3-D regions of significantreflectivity or other values above one or more specified thresholdvalues. Individual weather cells may be composed of reflectivity radialrun segments, and in turn, 2-D weather components composed of segmentgroups and occurring at different radar elevation angles. Weathercomponents with calculated mass weighted centroids may be verticallycorrelated into a cell with an established centroid. Such weather celldata may also include individual data points and trends for each weathercell. For example, current weather cell location may be provided withazimuth, range, direction, and speed information, such as a motionvector using polar and/or Cartesian coordinates along with an estimateof any tracking errors. Other examples include storm base height, stormtop height, maximum reflectivity, height of maximum reflectivity,probability of hail, probability of severe hail, cell-based verticallyintegrated liquid (VIL) content, enhanced echo tops (EET) and centroidheight. Weather tracking data may be generated by monitoring movement ofweather cells and matching cells in current and prior volume scans.Forecast data may be generated by predicting future centroid locationsbased on prior volume scans, and growth, decay, and/or shape changeestimates. Average data for multiple weather cells may be provided aswell (e.g., average motion vector data). The weather data may beprovided as, for example, a table of alphanumeric values, and/or as astand-alone display or graphical overlay.

In some embodiments, weather data indicative of weather conditionsexhibiting intense convection may include lightning data such as thatprovided by the NLDN. Such data may include indications of individualdischarges or flash rates in a given area. In some embodiments, pilotreports (PIREPs) may be used to indicate turbulence. In someembodiments, observation, nowcast and/or forecast data from weathersatellite systems, such as the Geostationary Operational EnvironmentalSatellite system (GOES) and Polar Operational Environmental Satellitesystem (POES) may also be used (e.g., to track volcanic ash cloudbehavior). In some embodiments, radiosonde data from weather balloonsmay be used. In some embodiments, data from satellite sources ornowcasting weather data sources (e.g., the Corridor Integrated WeatherSystem (CIWS)) may be used.

Referring to FIG. 4A, an exemplary weather radar system 400 is shown.System 400 may include a weather radar receiver/transmitter 402, weatherradar adjustable antenna 404, a memory 406 (e.g., a multi-scan,multi-tilt angle memory), a processor 408 and a system bus that couplesvarious system components including memory 406 to processor 408. System400 may also include a tilt control 409 for automatically controllingthe tilt angle (mechanical or electronic) of antenna 404. In someembodiments, this auto control may include an additional manual controlfeature as well. System 400 may also be in communication with one ormore displays 410 (e.g., a display similar to display 20 shown in FIG.1), one or more UI elements 411 (e.g., similar to UI elements 22 shownin FIG. 1) and one or more sensors 412, and also in communication withone or more remote data sources 414 (e.g., another aircraft or a groundstation) via a communications unit 416 (e.g., radio or other wirelesscommunication device).

Memory 406 may include any type of machine-readable storage devicecapable of storing radar returns or associated weather data 417 (shownin FIG. 4B) or program instructions for analysis/processing by processor408, such as weather image application 418 (shown in FIG. 4B). Memory406 may be, for example, a non-transitory machine-readable media forcarrying or having machine-executable instructions or data structuresstored thereon. Such machine-readable media may be any available mediathat may be accessed by a general purpose or special purpose computer orother machine with a processor. By way of example, such machine-readablemedia may comprise random access memory (RAM), read only memory (ROM),erasable programmable read only memory (EPROM), electrically erasableprogrammable memory (EEPROM), CD-ROM or other optical disk storage,magnetic disk storage or other magnetic storage devices, or any othermedium which may be used to carry or store desired program code in theform of machine-executable instructions or data structures and which maybe accessed by a general purpose or special purpose computer or othermachine. System 400 may have one or more memories 406 that use the sameor a different memory technology. Memory 406 may store weather data 417and weather image application 418 in addition to other instructions ordata.

In some embodiments, memory 406 may store in a readily addressable andrapidly retrievable manner at least two sets of weather data 417resulting from two or more antenna or radar beam sweeps at differentangles or tilt angles. Although a multi-scan, multi-tilt scanning anddata sets are described, it should be understood by one of ordinaryskill in the art that a single scan of data may also be used in someembodiments. Memory 406 may also include a three-dimensional storagebuffer for storing weather radar parameters according to X, Y and Zcoordinates according to one embodiment. The storage of radar data andthe form of the weather data 417 stored in the memory 406 is notdisclosed in a limiting fashion. A variety of techniques for storingweather data 417 may be used as well.

In some embodiments, weather data 417 may be stored (e.g., in the memory406) as a mathematical equation representation of the information. Themathematical equation representation may be a piecewise linear function,piecewise nonlinear function, coefficients of a cubic spline,coefficients of a polynomial function, etc., that represent verticalrepresentations of a weather condition based on the horizontal scan dataand/or horizontal representation of the weather condition based on thevertical scan data. The function may be an equation based on weatherparameters that may be sensor driven, model driven, a merger of sensorand model, etc. Although horizontal scan data is described, alternativeembodiments may include Cartesian coordinates, rho/theta input, latitudeand longitude coordinates, altitude, etc. Weather conditions may beestimated for any desired point in space with the vertical dimensionbeing the subject of the weather equation.

Processor 408 may be implemented in hardware, firmware, software, or anycombination of these methods. System 400 may have one or more processors408 that use the same or a different processing technology.Additionally, processor 408 may be a separate component of system 400 ormay be embedded within another component of system 400. Processor 408may execute instructions that may be written using one or moreprogramming languages, scripting languages, assembly languages, etc. Theinstructions may be carried out by, for example, a special purposecomputer, logic circuits, or hardware circuits. The term “execute” isthe process of running an application or the carrying out of theoperation called for by an instruction. Processor 106 may process dataand/or execute applications stored in memory 406, such as weather data417 and weather image application 418 and/or other instructions.

Processor 408 may be included as part of a multi-scan, multi-tilt angleweather radar system and may perform the customary functions performedby a conventional weather radar return processing unit. Processor 408may also perform several additional operations based upon the additionaldata and/or instructions provided in memory 406. In general, processor408 may merge or cross qualify portions, or ranges, of the radar returnsof several different antenna sweeps at several different tilt angles, sothat a single, relatively clutter-free image may be presented to thepilot based upon the several separate scans. The radar returns may beprocessed by processor 408 to generate a 2-D, 3-D, or 4-D weatherprofile of the weather near the aircraft. In some embodiments, processor408 may merge or cross qualify portions, or ranges, of the radar returnsor weather data of several different sources, including weather datafrom one or more remote sources 414, so that a composite or fused imagemay be presented to the pilot based upon the several weather datasources.

Processor 408 may process weather radar returns to identify or sense thepresence of weather conditions in front of (e.g., in the flight path) orin view of the aircraft. In some embodiments, processor 408 may utilizethe altitude and range of the weather condition to generate a verticalprofile associated with the weather. Processor 408 may scan across anarray of azimuths to generate a 3-D weather profile of the weather nearthe aircraft, which may be stored for later presentation and/ordisplayed on display 410. In some embodiments, additional visualindicators other than the representation of weather are provided ondisplay 410. In some embodiments, a range and bearing matrix havingrange markers indicating distance from a current location of theaircraft and bearing markers indicating azimuths from a current flightpath or bearing of the aircraft may be provided and may assist the pilotin cognitive recognition of weather features from the pilot'sperspective.

Referring now to FIG. 4B, a data flow diagram of exemplary weather radarsystem 400 is shown. As shown in FIG. 4B, processor 408 may provide avelocity parameter 420, such as a mean velocity parameter and a spectralwidth parameter 422 (e.g., derived from weather radar returns or fromweather data from a remote source for individual or grouped weathercells). Alternatively, other types of velocity parameters can beutilized. In addition, processor 408 may provide a reflectivityparameter 424 and a range parameter 426. Range parameter 426 along withscan angle position may be used to plot the location of a weathercondition on display 410. Processor 408 may also receive a temperatureparameter 430, an azimuth 431, a location 432, a date 433, a time 434,and a flight plan 435 from another source, such as an external sensor orsystem. In some embodiments, parameters 430, 431, 432, 433, 434 and 435may be computed by processor 408 using data stored in memory 406, suchas radar return data 417 a or remote source data 417 b.

Weather data 417 a from returns received by antenna 404 and weather data417 b from remote source 414 may be stored in memory 406. Weather data417 b from remote source 414 may be received via communications unit 416(shown in FIG. 4A). Weather data 417 may, for example, be based onreceived horizontal and/or vertical radar scans and/or data from othersources 414 (e.g., NEXRAD weather data). Weather data 417 may also befrom another weather radar source or data from an onboard weather radarsystem operating at a different frequency, such as a millimeterfrequency, a Ka band frequency, a W band frequency, etc. In someembodiments, weather data 417 may be from a non-radar airborne source (aLIDAR source, an infrared source, etc.). Weather data 417 may includeweather data as described with reference to FIG. 3 above. For example,weather data 417 may include a time of sensing data, such as a timestamp, and motion vector data (e.g., individual weather cell and averagemotion vector data) for temporal and spatial correlation (e.g., NEXRADdata received from remote source 414).

Referring again to FIG. 4A, memory 406 may store a weather imagingmodule 418 that may be executed by processor 408. Weather imaging module418 may be, for example, one or more program modules including routines,programs, objects, components, data structures, etc. that performparticular tasks or implement particular data types. Weather imagingmodule 418 may be written using, for example, one or more programminglanguages, assembly languages, scripting languages, etc. According to anexemplary embodiment, weather imaging module 418 may be an organized setof instructions that, when executed, cause processor 408 to utilizeweather data 417 a from returns received by antenna 404 and/or weatherdata 417 b received from remote source 414 stored in memory 406 toprovide individual, composite, fused, or overlay image data indicativeof a weather condition for display on display 410. The image dataderived from weather data 417 a and 417 b may be spatially correlated byweather imaging module 418 using, for example, time of sensinginformation and motion vector values. In some embodiments, growth anddecay information may be received, which may be used by weather imagingmodule 418 to increase or decrease the size, shape, and intensity of animage or other visual indication of a weather condition displayed inaccordance with time. In some embodiments, weather imaging module 418may determine a confidence factor reflecting the degree to which weatherdata 417 received from two or more sources agree in theircharacterization of the weather condition. In some embodiments, weatherimaging module 418 may combine estimates of storm top height receivedfrom two or more sources of weather data 417 to provide image dataindicative of the vertical extent of a weather condition.

Referring now to FIG. 5, a flow diagram of an exemplary process 500 forproviding image data indicative of a weather condition is shown. Process500 may be, for example, in the form of program instructions that may beexecuted by a processor included in a weather radar system, such asexemplary weather radar system 400 shown in FIGS. 4A and 4B. At a step510, weather data may be received from one or more sources as describedwith reference to, for example, FIGS. 3, 4A, and 4B.

At a step 520, the weather data may be filtered. For example, weatherdata that is outdated or corrupted may be removed. In some embodiments,radar returns data or other weather data indicative of convectiveweather conditions may be removed if they are older than one updatecycle. In some embodiments, lightning data provided as individual flashrecords may be managed such that flash density in a time window ofinterest may be determined, and flash records outside of the window ofinterest may be discarded. In some embodiments, data outside of adefined spatial coverage area may be removed. For example, if NEXRADdata is to be combined with other data sources, data from the othersources falling outside of a defined spatial coverage for the NEXRADdata may be removed or may be assigned a lower level of confidence. Insome embodiments, weather data within a certain range of the physicalradar source may be rejected. For example, data within 35 kilometers ofa radar source may typically be rejected where it may be assumed thatthe scan pattern results in inverted cones of silence in the radarreturns data, when the volume coverage pattern and altitude of theweather condition indicates a concern. In some embodiments, data fromone source may not be used if data from another source is deemed ofbetter quality. For example, data from a radar station using a clear airvolume coverage pattern may not be used if there is another stationusing a convective volume coverage for the same area, and convectivityis the primary focus.

At a step 530, weather data may be mapped to a common reference frame.In some embodiments, the received weather data may be mapped to a commongridded format using, for example, a 1 kilometer grid. Such a grid mayprovide a uniform structure in latitude and longitude to which one ormore sets of weather data from one or more sources may be mapped. Otherformats and grid spacings are contemplated as well, depending onrequired detail, available memory, and processing capabilities.

In some embodiments, weather data received in a non-gridded format(e.g., weather cell tracking data, lightning flash data, etc.) may belinked to the common gridded format. For example, referring now to FIG.6, an exemplary gridded weather data structure 600 is shown. Structure600 may include one or more grid points 602. Each grid point 602 maycorrespond to a grid point in a common gridded format. Each grid point602 may include corresponding data from gridded weather data sources(e.g., NEXRAD data, etc.). For example, each grid point 602 may includea latitude and longitude identifier 604, as well as corresponding valuessuch as a VIL value 606 and an EET value 608. Corresponding surfaceobservations 610 may also be mapped to each grid point 602. Each gridpoint 602 may also include a pointer to a list 620 of data structures622 for lightning strikes within a specified range of the grid point 602(e.g., for use in lightning density calculations). Data included in eachlightning strike data structure 622 may include, for example, a latitudeand longitude identifier 624 and a time stamp 626. Each grid point 602may also include a pointer to a list 630 of data structures 632 for oneor more weather cells (e.g., a NEXRAD weather cell) in which the gridpoint 602 is included. Data included in each weather cell data structure632 may include, for example, a latitude and longitude identifier 634, astorm height 636, a storm velocity or motion vector 638, and a list 640of grid points 602 within a specified reflectivity range, as well assegment or polygon data describing the shape of the weather cell. Otherexamples of data that may be included in each weather cell datastructure 632 may include a maximum registered height, a maximum VIL,indicators of hail presence, etc.

Referring again to FIG. 5, at a step 540 the weather data may beadjusted to update a location of a weather condition. For example,latencies or time delays associated with transmitting weather radar datamay lead to inaccuracies or discrepancies in the apparent location of aweather condition in or among uplinked weather data. The uplinkedweather data may be moved or advected in an appropriate distance anddirection from a first location (e.g., grid point) to an updated secondlocation based on data indicative of the movement of the weathercondition. For example, weather data, such as NEXRAD data, containinglocation, time of sensing, and motion vector data for a weathercondition may be received. The location of the weather condition may beadvected by a distance equal to the speed of the weather conditionmultiplied by an amount of time elapsed since the time of sensing of theweather data in an appropriate direction indicated by the motion vector.Weather data from different weather data sources may be advected usingthe same motion vector for each gridded location irrespective ofboundaries of an individual weather condition within each product. Theweather data may also be advected to a future time, such as forstrategic planning (e.g., based on forecasts from ground weather radardata compared with prediction from an aircraft weather radar system andforecasts from a satellite weather data source).

In some embodiments, the received weather data may include dataindicative of a respective first location for each of a plurality ofindividual weather cells, which may or may not be segmented. Thereceived weather data may further include a respective motion vector foreach weather cell. Each respective motion vector may include dataindicative of a speed and direction of travel for its correspondingweather cell, or for each grid location within a relevant region. Forsegmented weather cells, the respective motion vector for each cell maybe used to advect the weather cell in an appropriate distance anddirection from a first location to an updated second location. Forgridded weather data, the respective motion vectors for each griddedlocation may be used to advect the weather condition to an updatedsecond location for each grid point.

In some embodiments, an estimated motion vector may be determined for agrouping of the plurality of weather cells (e.g., based on an average,interpolation, a data quality analysis, etc.), and the estimated motionvector may be used to update the location of one or more of the weathercells in the grouping, the entire grouping, or weather cells outside thegrouping (e.g., non-segmented cells, cells without motion vector ortracking data, cells exhibiting erratic motion with respect tosurrounding cells, etc.). For example, a motion vector for the groupingof cells may be received from the weather data source (e.g., NEXRAD dataor data from an aircraft weather radar system) or may be determined froma plurality of individual motion vectors by the weather radar systemreceiving the weather data (e.g., based on an average, interpolation, adata quality analysis, etc.). The estimated motion vector may be appliedto one or more cells in the grouping, or to cells outside the grouping,such as an adjacent or nearby weather cell. In some embodiments whereweather data from multiple sources is to be combined, fused or overlaid,the estimated motion vector may be calculated using data for one or moreweather cells from a first one of the weather data sources and appliedto weather cells from other data sources (e.g., weather cells that maynot be correlated with weather cells in the weather data from the firstsource). In some embodiments, general atmospheric motion data fromnumerical analysis and forecasting may be used to provide a defaultmotion vector. In some embodiments the estimated motion vector may bereceived from a nowcasting or forecasting weather product, such as CIWS.In some embodiments where weather data from multiple sources may becombined to estimate motion vectors for each grid location, a decisionbased on the quality of the weather data may be made. In someembodiments where weather data is not available for all grid locations,general atmospheric motion data from numerical analysis and forecastingmay be used to provide a default motion vector for certain gridlocations.

In some embodiments, uncertainty in the advection process may bereflected by adjusting the size of a weather condition indicator. Forexample, the size of a weather condition indicator may be increased inproportion with the distance that the weather condition may be advected.Similarly, the perimeter of a weather cell may be increased by apredetermined number of grid points or image pixels (e.g., one gridpoint or pixel) to account for changes such as growth or decay or achange in shape. In some embodiments, a weather data source may provideweather data including an estimate of possible errors in the trackingprocess for one or more weather cells, or an estimate of a change insize of the weather condition due to growth or decay. The error estimateor change estimate may be used to adjust the size of the weathercondition indicator. In some embodiments, overlay images may begenerated to indicate estimated error or growth and decay. In someembodiments, the size of a weather condition indicator may be increasedor decreased according to the estimated error and/or growth or decay,and/or the severity level indicated by the weather condition indicatormay be increased or decreased. In some embodiments, when divergentmotion vectors will result in gaps in advected data, each gap may beevaluated based on the motion vectors of adjacent grid points. In someembodiments, multiple weather data sources having a common time and gridmay be advected using a common motion vector.

In some situations, the received weather data may include one or moreweather cells with motion vectors indicating motion significantlydifferent from that of adjacent or nearby weather cells (e.g., due tocollapse of a large weather cell generating a diverging pool of cold airthat spawns new cells around its perimeter. In some embodiments, thismay be addressed by analyzing the tracking history or determining aconfidence estimate for any divergent weather cells. For divergentweather cells with a short tracking history (e.g., based on a minimumthreshold of samples), an average motion vector for a grouping ofsurrounding or nearby cells may be calculated and used for advecting thedivergent weather cell. For divergent weather cells with a sufficientnumber of samples, the motion vector for the weather cell may be used toadvect the weather cell to an updated location. In some embodiments,both the average and actual motion vectors may be used to advect thedivergent weather cell to two locations per the calculated distances foreach motion vector, and an image of the divergent weather cell may beincreased to cover both advected locations.

Referring now to FIG. 7, an exemplary process for adjusting weather datato update a location of a weather condition is shown. At a step 710,weather data including data indicative of a first location for one ormore weather cells associated with a weather condition may be received.At a step 720, a second location for one or more of the weather cellsmay be determined based on data indicative of movement of the weathercell. Data indicative of movement of the weather cell may include, forexample, actual or average motion vectors received or calculated per oneor more of the various embodiments described herein. At a step 730, thesize of one or more images for the one or more weather cells may beadjusted to account for, for example, estimated errors or changes insize or shape for the weather cell. At a step 740, an image of the oneor more weather cells may be displayed with reference to the secondposition according to one or more of the various embodiments describedherein.

In some embodiments, weather data for a weather condition may bereceived from multiple unsynchronized sources (e.g., multiple NEXRADradar installations). For example, an aircraft having an aircraft-basedweather radar system and receiving weather data from a ground-basedweather radar system may be moving from the radar coverage area of oneground-based system to another ground-based system. In such embodiments,the location of a weather condition may be advected using weather datafrom whichever data source provides the best tracking geometry. Forexample, the respective range of each of the weather data sources may beused to determine which source may provide the best source of data foradvection (e.g., azimuth resolution may deteriorate with increasingdistance, data near a data source may be limited due to a low maximumradar elevation range, etc.). In some embodiments, an estimated motionvector may be calculated using data from one or more or all of theavailable data sources within range of the weather condition. In someembodiments, multilateration may be applied to range data received fromeach of the weather data sources rather than using motion vector data inorder to avoid azimuth resolution issues. In some embodiments, thevolume coverage pattern, altitude, range, and age of the weather datafor each weather data source may be used to estimate a number of radarslices or beams in order to determine the quality of the weather datafrom each source.

Referring now to FIG. 8, an exemplary process 800 for selecting amongweather data sources in order to update a location of a weathercondition is shown. At a step 810, weather data indicative of a firstlocation and other parameters for one or more weather cells associatedwith a weather condition may be received from one or more weather datasources. At a step 820, the location data for weather cells from eachsource may be advected to a common time (e.g., the time that the mostrecent radar volume scan was completed). At a step 830, the advectedweather cell locations from each weather data source within a particularregion of interest may be compared to determine correspondence. Forexample, in some embodiments, global weather cell locations may becreated and assigned to all cells within a predetermined distance (e.g.,a set number of nautical miles) of each other such that weather cellsfrom each source within the predetermined distance from each other areassumed to be corresponding weather cells for purposes of evaluation.

A quality estimate may be determined for weather data from each weatherdata source. For example, at a step 840, a first criterion may beevaluated on a cell-by-cell basis for weather data received from each ofthe weather data sources for each corresponding cell. In someembodiments, the first criterion may be indicative of negative factorsassociated with the weather data for each corresponding weather cellfrom each data source. According to an exemplary embodiment, thesefactors may include, but are not limited to one or more of thefollowing: (a) absence of tracking data; (b) data aged beyond apredetermined limit (e.g., 15 minutes); (c) direction data receivedwithout a speed value; (d) the difference between a minimum acceptablenumber of elevation scans (e.g., five beams) and the number of scansthat fall below 40,000 feet of the cell location; (e) tracking errorestimates provided by the data source (e.g., NEXRAD error estimates);(f) absence of forecast data; (g) absence of cell structure data; and(h) data outside of a maximum allowable distance range for the weatherdata source. Appropriate weights may be applied to each factor. Thevalue of the first criterion for each corresponding cell from eachweather data source may be compared with a threshold value, and celldata from a source with a value of the first criterion greater than thethreshold value may be discarded. If no weather data from any weatherdata source for a particular cell falls below the threshold value, thenthe weather cell from the source having the lowest value may be selectedfor further evaluation. If weather data for one or more correspondingweather cells falls below the threshold, then each of these weathercells may be selected for further evaluation.

At a step 850, a second criterion may be evaluated on a cell-by-cellbasis for corresponding weather cells from each weather data sourceselected for further evaluation. In some embodiments, the secondcriterion may be indicative of positive factors associated with the datafor each corresponding weather cell from each data source. According toan exemplary embodiment, more highly weighted factors may include, butare not limited to, one or more of the following: (a) the number offorecast values provided (e.g., for NEXRAD data, the number of 15, 30,45, and 60 minute forecasts received, with higher weighting assigned tothe 15 and 30 minute forecasts); (b) the estimated number of beams inthe volume scan pattern that fall below a particular aircraft altitude(e.g., 40,000 feet) at the range of the cell multiplied by a weightingfactor; and (c) volume scan patterns used under differing ambientconditions (e.g., a higher value may be assigned to a higher number ofscans and a higher sweep rate used by NEXRAD for convective weather, anda lower value to a lower number of scans or a lower scan rate used byNEXRAD in stratiform rain, clear air, or winter modes). In someembodiments, lower weighted factors may include: (d) freshness of data(e.g., decremented by the difference between the current time and thetime of sensing, with a linear decrement from no decrement for freshdata to a score of zero for data of the maximum allowable age (e.g., 15minutes)); (e) forecast motion accuracy, with a decreasing value as theerror approaches a maximum acceptable error (e.g., 5 nautical miles);(f) VIL for each cell; and (g) maximum radar reflectivity for each cell.Appropriate weights may be applied to each factor. The value of theweighted sum of these factors for each corresponding cell may be used toselect the data source for each weather cell having the highest value.

At a step 860, one or more of the weather cells evaluated in steps 840and 850 may be selected as providing the best weather data based on theevaluation. For example, a composite set of motion vector data may begenerated using the data for each weather cell selected based on theevaluations in steps 840 and 850. At a step 870 each weather cell may beadvected to an updated location for display. In some embodiments, if noweather cell from any weather data source for a particular cell is foundacceptable, then a motion vector (e.g., an average motion vector, amotion vector based on a quality estimate, etc.) may be calculated froma grouping of adjacent or nearby cells and used to advect thatparticular cell.

In some situations, weather data received from a weather data source mayinclude one or more weather cells without motion vector or trackingdata. In some embodiments, a motion vector based on all other cellshaving motion vector data may be used as described above, provided thatthe region of interest represented by the weather cell does not exhibitstrong rotation (e.g., in the presence of a strong low-pressure area).In some embodiments, a more accurate estimate may be achieved bycalculating the motion vector based on adjacent or nearby cells asdescribed above. In some embodiments, the motion vector may be based onweather model information (e.g., High Resolution Rapid Refresh, WeatherResearch and Forecasting, etc.).

Referring now to FIG. 9, an exemplary process 900 for updating thelocation of weather cells not having motion vector or tracking data isshown. At step 910, weather data indicative of a first location andother parameters, such as motion vector data, for one or more weathercells associated with a weather condition in a region of interest may bereceived from one or more weather data sources. At a step 920, a uniformgrid pattern may be generated for the region of interest. For example,the region of interest may be divided into squares 1 kilometer to aside. At a step 930, motion vector data for each respective weather cellmay be assigned to one or more grid points near the weather cell. At astep 940, linear interpolation may be used to assign motion vector datato any unassigned grid points between grid points having assigned motionvector data. At a step 950, motion vector data may be estimated for anyunassigned grid points outside of grid points having assigned motionvector data. In some embodiments, linear extrapolation may be used toprovide the estimate. In some embodiments, an average motion vector forthe assigned grid points may be used to provide the estimate. In someembodiments, a least squares fit of a 2-D polynomial function based onthe assigned grid points may be used to provide the estimate. Using aleast squares fit may provide the advantage of smoothing over erraticweather cell motion. In some embodiments, weather data derived fromradiosonde data, weather models (e.g., High Resolution Rapid Refresh,Weather Research and Forecasting, etc.), or from NEXRAD Storm Trackingmay be used. At a step 960, the location of each grid point may beupdated using the assigned motion vector data. At a step 970, an imageof a weather condition associated with the weather cells may begenerated with respect to the updated location for each weather cell.

Referring again to FIG. 5, at a step 550, the weather data may beadjusted to a common hazard scale. In some embodiments, weather datareceived from a remote weather data source may be translated to thescale used by the receiving weather radar system. In some embodiments,the weather data may be translated according to the Federal AviationAdministration (FAA) VIP hazard scale. Referring to FIG. 10, anexemplary translation chart 1000 of selected parameters to the VIPhazard scale is shown. An exemplary approach to estimating VIL fromairborne weather radar reflectivity measurements is described in U.S.patent application Ser. No. 14/086,844 filed on Nov. 21, 2013 byBreiholz et al., entitled “Weather Radar System and Method forEstimating Vertically Integrated Liquid Content,” assigned to theassignee of the present application and incorporated herein by referencein its entirety.

Referring again to FIG. 5, at a step 560, a hazard level may bedetermined for each grid point. In some embodiments, a VIP value may beassigned to each grid point. Factors in assigning each VIP value mayinclude, for example, radar reflectivity and VIL values received fromone or more weather data sources. In some embodiments, compositereflectivity may be substituted where VIL is not available, or may beused to supplement VIL in situations where composite reflectivity may beupdated more frequently. In some embodiments, a maximum VIP value may beassigned to each grid point where it is the maximum value available fromall weather data sources for each particular grid point. In someembodiments, a maximum VIP may be assigned at each grid point providedthat each value is logically consistent. For example, maximum VIP valuesof 2 or less may not need to be checked for consistency. Higher VIPvalues may be deemed consistent if weather data from other weather datasources or adjacent grid points is one VIP level below the maximum orhigher as may be consistent with the continuous nature of convectiveactivity, e.g., high threat values may not exist in isolation with nosurrounding detectable weather. In some embodiments, a confidence levelassociated with each particular weather data source may be used todetermine which source to use to assign the VIP value to a particulargrid point, such that the VIP value with the highest confidence is used.In some embodiments, the VIP value may be assigned based in part on oneor more applicable guidelines. Guidelines may include, for example,regulations (e.g., RTCA DO-340 guidelines establishing a particularweather data source for a given flight scenario, planning horizon, otherrules and regulations, etc.). In some embodiments, a VIP value may beassigned based on guidelines for a particular flight scenario (e.g.,en-route, descent, departure, etc.) and/or if a particular grid point iswithin a particular planning horizon or distance from the aircraft(e.g., less than 3 minutes, 3-20 minutes, more than 20 minutes, etc.).In some embodiments, guidelines may include guidelines for particularweather data sources that may be relevant for a given flight scenarioand/or planning horizon (e.g., observations, nowcasts and/or forecastsfrom Airborne Weather Sensors, CoSPA, NEXRAD, SIGMETS, GraphicalTurbulence Guidance, etc.), and a particular weather data source may beselected based on, for example, the weather data source indicating themaximum VIP value for a particular grid point. In some embodiments, theVIP value may be based on guidelines setting priorities for flightscenarios and/or the expected risk of encountering specific atmosphericthreats during that flight scenario or phase of flight. In someembodiments, a VIP value may be assigned to a particular grid pointbased on an average of two or more sensors or weather data sources. Insome embodiments, a confidence value for a selected VIP value may beused to adjust one or more final VIP values (e.g., a low confidence VIPvalue may be increased to a higher VIP value, a high VIP value with alow confidence value may be used to increase adjacent VIP values toallow a wider berth, etc.).

In some embodiments, lightning data received from one or more weatherdata sources may be factored in the assignment of each VIP value. Asshown in FIG. 10, the mapping of lightning rate to VIP is based on anobservation window of 10 minutes within an 8 kilometer radius of eachgrid point. In some embodiments, this radius may be larger than gridspacing such that a given lightning strike may contribute to thelightning rate at multiple grid points. The exemplary mapping oflightning strike density to VIP shown in FIG. 10 is based on theNational Weather Association National Convective Weather Forecastalgorithm. In some embodiments, the observation window and/or radius maybe adjusted to prevent the lighting factor from dominating theassignment of VIP values. In some embodiments, data from anaircraft-based lightning detector may be included, provided that rangeestimates from the lightning detector are checked for consistency with,for example, weather radar data for nearby weather cells.

A VIP value or other hazard level may also be applied to other weatherdata, such as observations, nowcasts, and/or forecasts of turbulence,icing, High Altitude Ice Water Content areas, volcanic ash, etc. In someembodiments, inferred hail data received from one or more weather datasources may be factored in the assignment of each VIP value. Forexample, given that hail is indicative of severe weather (e.g., a VIPvalue of 3 or higher), indication of the presence of hail may beevidence that a weather cell is hazardous. In some embodiments, NEXRADinferred hail data using multiple elevation angles and referenced to thefreezing and −20° C. levels may be used. Similarly, inferred lightningdata (e.g., from an aircraft-based weather radar system) may be factoredinto the assignment of each VIP value. In some embodiments, VIP threatinformation for turbulence may be used (e.g., Graphical TurbulenceGuidance data, aircraft-based radar data). In some embodiments, icingdata from in-situ icing detectors (e.g., own-ship or other aircraft) orweather data sources may be used. Volcanic ash information may comefrom, for example, an infrared detection system (e.g., own-ship, anotheraircraft, etc.) or derived from satellite weather data sources. In someembodiments, consistency of each VIP level or value may be cross-checkedwith a received echo top height measurement from one or more weatherdata sources, given that heavy stratiform rain systems may have heavyrainfall, but lack the high storm top altitudes associated with severeconvective weather systems.

In some embodiments, sounding data received from one or more weatherradar data sources may be factored into the assignment of each VIPvalue. Sounding data may generally provide profiles of temperature, dewpoint, and wind speed and direction as a function of altitude, and mayprovide insight into the likelihood and severity of convective weather.For example, the region between freezing and −20° C. may provide thehighest radar reflectivity due to the increased likelihood of liquidcoated hail in this region. The tropopause height may generally indicatehow high air will continue to rise during convection. Exemplary indicesderived from sounding data that may be indicative of convective weathermay include Convective Available Potential Energy (CAPE) and ConvectiveInhibition (CIN). High CAPE may indicate the likelihood of intensestorms. High CIN may indicate that a greater atmospheric disturbancewill be required to initiate convective weather, but also that ifconvection does occur, it may be more intense.

In some embodiments, data from an aircraft-based weather radar system,such as ambient temperature, altitude, air pressure, and wind directionand speed may be factored into the assignment of VIP values. In someembodiments, this data may be used in conjunction with sounding data,analyses, or forecasts to adjust estimated atmospheric profiles basedon, for example, 12-hour soundings to the current time to provideimproved estimates of parameters such as freezing level or tropopauseheight. In some embodiments, weather data for a predetermined distancebelow the altitude of the aircraft, (e.g., 10,000 feet), may besuppressed as being less hazardous to the aircraft.

In some embodiments, other parameters may be factored into theassignment of each VIP value. For example, in some embodiments, reportedsurface data may be used in conjunction with aircraft-based air datameasurements to estimate current atmospheric temperature profiles. Insome embodiments, satellite-derived weather data may be used, including,for example, images formed at multiple visible and infrared wavelengths.These images may be used individually or combined together with otherweather data for specific purposes targeted at convective activity suchas cloud cover, cloud top temperature (from which cloud top height maybe inferred), and convective initiation. A determination of cloud covermay be useful in removing spurious data from weather data. In someembodiments, the cloud cover image may be used as a mask, which may beadvected to compensate for latency, and used to mask out weather radarreturns from cloudless areas. Such returns may be due to flocks of birdsor insects or to atmospheric anomalies or solar interference. In someembodiments, cloud top height derived from the measured temperature ofthe clouds referenced to the atmospheric temperature profile derivedfrom soundings may be used in lieu of or in addition to radar cloud topdata to gauge the severity of storms and to determine their relevance toaircraft flying at high altitudes. In some embodiments, convectiveinitiation may be used as an early indicator of convective activity andas a source of information about convection beyond the range of airborneweather radar in parts of the world where ground radar data may not beavailable.

Referring again to FIG. 5, at a step 570, a confidence level may beevaluated for the hazard level determined for each grid point. Theconfidence level may be a value that increases with the number ofweather data sources in agreement for a given grid point. In someembodiments the confidence level may be based on weather data for eachspecific grid point. In some embodiments, the confidence level may bebased on weather data within a region of interest surrounding aparticular grid point. According to an exemplary embodiment, aconfidence level for each grid point may be evaluated using weather datawithin two grid points of the particular grid point being evaluated,such that a 5×5 matrix of grid squares centered about the particulargrid point being evaluated are examined.

According to an exemplary embodiment, an acceptable match betweenweather data sources may be defined by any value greater than two lessthan the value of the data being evaluated at a particular grid point.Under this acceptable match definition, surrounding values higher thanthe value of the data being evaluated at a particular grid point arealways counted as support. In some embodiments, more complex variationson both the region of interest and the acceptable match definition maybe implemented by, for example, assigning lower weights to more distantmatches or defining fuzzy logic membership functions around the databeing evaluated at a particular grid point.

A confidence level of 1.0 may be output for a particular grid point ifan acceptable match exists for a certain number or percentage ofsurrounding weather data values (e.g., 40%). For example, if threeweather data sources are used with a region of interest of a 5×5 matrixof grid squares centered about the particular grid point beingevaluated, there are three 5×5 matrices of weather data values (75 totalvalues). The center point of one of these matrices contributes the valueof the data being evaluated (e.g., a VIP value), leaving 74 values thatmay support the value of the data being evaluated. If the acceptablematch percentage is set at 40%, then 30 of these values must meet theacceptable match definition (e.g., 30 values that are greater than twoless than the value of the data being evaluated at a particular gridpoint) in order to achieve a confidence level of 1.0.

A curve may be defined to set the confidence level for cases where fewerthan the required number or percentage of surrounding weather datavalues (e.g., 40%) support the value of the weather data beingevaluated. In some embodiments, a linear curve may be used. In someembodiments, an exponential curve may be used. For example, where therequired percentage of surrounding weather data values is 40%, anexemplary exponential rise may be defined by C=10^((Count))/10^((0.40))where C is the confidence level and Count is the percentage ofsurrounding weather data values that support the value of the weatherdata being evaluated.

In some embodiments, the various weather data sources may be weightedaccording to their reliability at a given grid point. For example, anaircraft-based weather radar may be weighted more heavily than otherweather data sources within a certain range of the aircraft (e.g., 80nautical miles) due to a faster update rate, but may be weighted lowerthan other weather data sources at greater ranges where it may besubject to beam spreading, shadowing, or ground clutter.

Referring again to FIG. 5, at a step 580, an image of a weathercondition may be generated for display. Images may be displayed usingone or more colors (e.g., red, yellow, and green) to indicate areas ofrespective precipitation rates, and black to indicate areas of verylittle or no precipitation. Each color may be associated with a radarreflectivity range which corresponds to a respective precipitation raterange. For example, red may indicate the highest rates of precipitationwhile green may indicate the lowest (non-zero) rates of precipitation.Certain displays may also utilize a magenta color to indicate regions ofturbulence. In some embodiments, images may be displayed using outlines,cross-hatching, uniformly organized speckles, etc. The outlines,cross-hatching and/or speckles may use color (e.g., to indicateprecipitation rate). The images may be bounded (e.g., outlined) orunbounded and may have a variety of shape profiles including rounded andstraight edges.

In some embodiments, the images may provide an indication of theconfidence levels determined at step 570. For example, in someembodiments, a separate image is generated for purposes of portrayingconfidence values. In some embodiments, the color saturation orbrightness of a portion of an image may be reduced to indicate regionsof lower confidence. In some embodiments, a cross-hatch pattern may beused to distinguish regions of lower confidence, depending on thecapabilities of the display.

Some ground-based weather data sources, such as NEXRAD, provide echo topheight data in addition to identifying individual weather cells anddetermining cell height and VIL for each. Similarly, some aircraft-basedweather radar systems may perform vertical sweeps through areas of highreflectivity to provide estimates of echo top height. In someembodiments, asynchronous ground-based and aircraft-based echo topheight data may be fused to adjust older, but more detailed ground baseddata.

Referring now to FIGS. 11A-11F, exemplary approaches to fusion of echotop height data are shown. FIG. 11A shows an exemplary echo top image1100 from an aircraft-based weather system with the vertical measurementindicated at 1102. FIG. 11B shows a 2-D rendering of ground-based echotops data 1104 with the aircraft-based vertical measurement 1102overlaid. FIG. 11C shows an exemplary approach to echo top fusionwherein the top of curve 1104 is raised to match the height indicated bythe aircraft-based vertical measurement 1102. FIG. 11D illustratesanother exemplary approach to echo top fusion wherein the shape of curve1104 is retained but raised so that its peak matches the heightindicated by the aircraft-based vertical measurement 1102. FIG. 11Eillustrates another exemplary approach to echo top fusion wherein theoffset of curve 1104 as shown in FIG. 11D is gradually reduced to smooththe transition moving away from the region corresponding to the heightindicated by the aircraft-based vertical measurement 1102. FIG. 11Fillustrates another exemplary approach to echo top fusion wherein theentire curve 1104 is raised to match the height indicated by theaircraft-based vertical measurement 1102. The approach illustrated inFIG. 11F may be modified to use linear interpolation where multiple topmeasurements exist that are not uniformly offset. In each of FIGS.11B-11F, a region 1106 indicates the increased safety margin due toupdating the ground based estimate with the aircraft-based measurement,and a region 1108 indicates additional airspace identified as safe withrespect to the aircraft-based measurement alone. In some embodiments,the approaches illustrated in FIGS. 11B-11F may be applied where theaircraft-based echo top height measurement is lower than theground-based echo top height measurement. In some embodiments, loweringthe height is only permitted if there is a well-defined lowering of theheight exhibited over multiple samples.

In some embodiments, the fused echo top data may be displayed as aseparate color-coded relief map. In some embodiments, estimated tops ofindividual cells may be indicated as numerical tags on a display, suchas a reflectivity/VIL VIP display. In some embodiments, a 3-D displaymay be generated to represent an individual polygon for each grid point.In some embodiments, data more than a predetermined threshold distance(e.g., 10,000 feet) below the aircraft may be suppressed or otherwisevisually indicated.

The embodiments in the present disclosure have been described withreference to drawings. The drawings illustrate certain details ofspecific embodiments that implement the systems and methods and programsof the present disclosure. However, describing the embodiments withdrawings should not be construed as imposing any limitations that may bepresent in the drawings. The present disclosure contemplates methods,systems and program products on any machine-readable media foraccomplishing its operations. The embodiments of the present disclosuremay be implemented using an existing computer processor, or by a specialpurpose computer processor incorporated for this or another purpose orby a hardwired system.

As noted above, embodiments within the scope of the present inventioninclude program products comprising non-transitory machine-readablemedia for carrying or having machine-executable instructions or datastructures stored thereon. Such machine-readable media may be anyavailable media that may be accessed by a general purpose or specialpurpose computer or other machine with a processor. By way of example,such machine-readable media may comprise RAM, ROM, EPROM, EEPROM, CD-ROMor other optical disk storage, magnetic disk storage or other magneticstorage devices, or any other medium which may be used to carry or storedesired program code in the form of machine-executable instructions ordata structures and which may be accessed by a general purpose orspecial purpose computer or other machine with a processor. Thus, anysuch a connection is properly termed a machine-readable medium.Combinations of the above are also included within the scope ofmachine-readable media. Machine-executable instructions comprise, forexample, instructions and data which cause a general purpose computer,special purpose computer, or special purpose processing machines toperform a certain function or group of functions.

Embodiments in the present disclosure have been described in the generalcontext of method steps which may be implemented in one embodiment by aprogram product including machine-executable instructions, such asprogram code, for example, in the form of program modules executed bymachines in networked environments. Generally, program modules includeroutines, programs, objects, components, data structures, etc. thatperform particular tasks or implement particular abstract data types.Machine-executable instructions, associated data structures, and programmodules represent examples of program code for executing steps of themethods disclosed herein. The particular sequence of such executableinstructions or associated data structures represent examples ofcorresponding acts for implementing the functions described in suchsteps.

As previously indicated, embodiments in the present disclosure may bepracticed in a networked environment using logical connections to one ormore remote computers having processors. Those skilled in the art willappreciate that such network computing environments may encompass manytypes of computers, including personal computers, hand-held devices,multi-processor systems, microprocessor-based or programmable consumerelectronics, network PCs, minicomputers, mainframe computers, and so on.Embodiments in the disclosure may also be practiced in distributedcomputing environments where tasks are performed by local and remoteprocessing devices that are linked (either by hardwired links, wirelesslinks, or by a combination of hardwired or wireless links) through acommunications network. In a distributed computing environment, programmodules may be located in both local and remote memory storage devices.

An exemplary system for implementing the overall system or portions ofthe disclosure might include one or more computers including aprocessor, a system memory or database, and a system bus that couplesvarious system components including the system memory to the processor.The database or system memory may include read only memory (ROM) andrandom access memory (RAM). The database may also include a magnetichard disk drive for reading from and writing to a magnetic hard disk, amagnetic disk drive for reading from or writing to a removable magneticdisk, and an optical disk drive for reading from or writing to aremovable optical disk such as a CD ROM or other optical media. Thedrives and their associated machine-readable media provide nonvolatilestorage of machine-executable instructions, data structures, programmodules and other data for the computer. User interfaces, as describedherein, may include a computer with monitor, keyboard, a keypad, amouse, joystick or other input devices performing a similar function.

It should be noted that although the diagrams herein may show a specificorder and composition of method steps, it is understood that the orderof these steps may differ from what is depicted. For example, two ormore steps may be performed concurrently or with partial concurrence.Also, some method steps that are performed as discrete steps may becombined, steps being performed as a combined step may be separated intodiscrete steps, the sequence of certain processes may be reversed orotherwise varied, and the nature or number of discrete processes may bealtered or varied. The order or sequence of any element or apparatus maybe varied or substituted according to alternative embodiments.Accordingly, all such modifications are intended to be included withinthe scope of the present disclosure. Such variations will depend on thesoftware and hardware systems chosen and on designer choice. It isunderstood that all such variations are within the scope of thedisclosure. Likewise, software and web implementations of the presentinvention could be accomplished with standard programming techniqueswith rule based logic and other logic to accomplish the various databasesearching steps, correlation steps, comparison steps and decision steps.

The foregoing description of embodiments has been presented for purposesof illustration and description. It is not intended to be exhaustive orto limit the subject matter to the precise form disclosed, andmodifications and variations are possible in light of the aboveteachings or may be acquired from practice of the subject matterdisclosed herein. The embodiments were chosen and described in order toexplain the principals of the disclosed subject matter and its practicalapplication to enable one skilled in the art to utilize the disclosedsubject matter in various embodiments and with various modifications asare suited to the particular use contemplated. Other substitutions,modifications, changes and omissions may be made in the design,operating conditions and arrangement of the embodiments withoutdeparting from the scope of the presently disclosed subject matter.

Throughout the specification, numerous advantages of the exemplaryembodiments have been identified. It will be understood, of course, thatit is possible to employ the teachings herein without necessarilyachieving the same advantages. Additionally, although many features havebeen described in the context of a particular data processor, it will beappreciated that such features could also be implemented in the contextof other hardware configurations.

While the exemplary embodiments illustrated in the figures and describedabove are presently preferred, it should be understood that theseembodiments are offered by way of example only. Other embodiments mayinclude, for example, structures with different data mapping ordifferent data. The disclosed subject matter is not limited to aparticular embodiment, but extends to various modifications,combinations, and permutations that nevertheless fall within the scopeand spirit of the appended claims.

What is claimed is:
 1. A method of displaying an image representative ofa weather condition near an aircraft, the method comprising: receivingweather data representative of the weather condition from a plurality ofweather data sources, the weather data including location data for theweather condition; mapping the weather data received from each source toa common locational reference frame based on the location data;adjusting the weather data received from each source to a common hazardscale; determining a hazard level associated with the weather conditionfor a reference point in the reference frame based on the adjustedweather data for each source; and displaying the image representative ofthe weather condition near the aircraft based on the hazard level forthe reference point; wherein adjusting the weather data comprisesadjusting a portion of the weather data received from a first source ofthe plurality of weather data sources based on a characteristic of theweather condition indicated by the weather data received from a secondsource of the plurality of weather data sources, and adjusting theweather data by retaining the shape of a curve defined by the portion ofthe weather data received from the first source.
 2. The method of claim1, further comprising adjusting the weather data from one of the weatherdata sources to update a location of the weather condition within thereference frame.
 3. The method of claim 2, wherein the weather data fromthe one of the sources includes a motion vector, the method furthercomprising adjusting the weather data to update the location based onthe motion vector.
 4. The method of claim 1, further comprisingevaluating a confidence level for the hazard level at the referencepoint.
 5. The method of claim 4, wherein the confidence level is basedon weather data within a region of interest surrounding the referencepoint.
 6. The method of claim 1, further comprising filtering theweather data received from each weather data source.
 7. The method ofclaim 6, wherein the weather data received from each weather data sourceis filtered based on one of a time of sensing, a range of the weathercondition from the weather data source from which the respective weatherdata is received, and a defined spatial coverage area for at least oneof the weather data sources.
 8. The method of claim 1, wherein thecommon locational reference frame is a gridded reference frame includinga plurality of grid points.
 9. The method of claim 8, wherein weatherdata received in a non-gridded format is linked to the gridded referenceframe using a gridded weather data structure.
 10. The method of claim 9,wherein the non-gridded weather data includes one of weather celltracking data and lightning flash data.
 11. The method of claim 1,wherein adjusting the weather data received from each source to a commonhazard scale includes adjusting one of rainfall rate data, reflectivitydata, and vertically integrated liquid.
 12. The method of claim 1,wherein the hazard level is determined based on one of turbulence data,reflectivity data, vertically integrated liquid data and lighting data.13. The method of claim 1, wherein determining the hazard levelassociated with the weather condition for the reference point in thereference frame includes at least one of determining a maximum hazardvalue available for the reference point from each weather data source,selecting one of the weather data sources based on a confidence levelfor each weather data source, and selecting one of the weather datasources based in part on one or more applicable guidelines.
 14. Anaircraft weather radar system, comprising: a processor; and anon-transitory memory coupled to the processor and containing programinstructions that, when executed, cause the processor to receive weatherdata representative of a weather condition from a plurality of weatherdata sources, the weather data including location data for the weathercondition; map the weather data received from each source to a commonlocational reference frame based on the location data; determine ahazard level associated with the weather condition for a reference pointin the reference frame based on the adjusted weather data for eachsource; evaluate a confidence level for the hazard level at thereference point; and display an image representative of the weathercondition near the aircraft based on the confidence level for the hazardlevel at the reference point; wherein the program instructions areconfigured to cause the processor to adjust the weather data byretaining a shape of the weather condition defined by a portion of theweather data received from a first source of the plurality of weatherdata sources based on a characteristic indicated by the weather datareceived from a second source of the plurality of weather data sources.15. The system of claim 14, wherein the program instructions are furtherconfigured to cause the processor to adjust the weather data receivedfrom each source to a common hazard scale.
 16. The system of claim 14,wherein the program instructions are further configured to cause theprocessor to evaluate the confidence level based on weather data withina region of interest surrounding the reference point.
 17. The system ofclaim 14, wherein the program instructions are further configured tocause the processor to evaluate the confidence level using anexponential function.
 18. A weather radar system, comprising: aprocessor; and a non-transitory memory coupled to the processor andcontaining program instructions that, when executed, cause the processorto receive echo top data for a weather condition from first and secondweather data sources; map the echo top data to a common reference frame;adjust the echo top data received from the first source based on theecho top data received from the second source; and generate an imagerepresentative of the weather condition based on the adjusted echo topdata; wherein the program instructions are configured to cause theprocessor to adjust the echo top data by increasing values of a portionof the echo top data received from the first source based on a height ofthe weather condition indicated by the echo top data received from thesecond source; wherein the program instructions are configured to causethe processor to adjust the echo top data by retaining the shape of acurve defined by the portion of the echo top data received from thefirst source in the adjusted data.
 19. The system of claim 18, whereinthe received echo top data is received in a non-gridded format, andwherein the common reference frame has a common gridded format.
 20. Themethod of claim 19, wherein the common gridded format comprises aplurality of grid points, each grid point corresponding to a latitudeand longitude identifier, and wherein a surface observation is mapped toeach grid point.